%0 Conference Proceedings
%T ChaLearn LAP 2020 Challenge on Identity-preserved Human Detection: Dataset and Results
%A Albert Clapes
%A Julio C. S. Jacques Junior
%A Carla Morral
%A Sergio Escalera
%B 15th IEEE International Conference on Automatic Face and Gesture Recognition
%D 2020
%F Albert Clapes2020
%O HUPBA
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3501), last updated on Tue, 20 Sep 2022 15:14:05 +0200
%X This paper summarizes the ChaLearn Looking at People 2020 Challenge on Identity-preserved Human Detection (IPHD). For the purpose, we released a large novel dataset containing more than 112K pairs of spatiotemporally aligned depth and thermal frames (and 175K instances of humans) sampled from 780 sequences. The sequences contain hundreds of non-identifiable people appearing in a mix of in-the-wild and scripted scenarios recorded in public and private places. The competition was divided into three tracks depending on the modalities exploited for the detection: (1) depth, (2) thermal, and (3) depth-thermal fusion. Color was also captured but only used to facilitate the groundtruth annotation. Still the temporal synchronization of three sensory devices is challenging, so bad temporal matches across modalities can occur. Hence, the labels provided should considered “weak”, although test frames were carefully selected to minimize this effect and ensure the fairest comparison of the participants’ results. Despite this added difficulty, the results got by the participants demonstrate current fully-supervised methods can deal with that and achieve outstanding detection performance when measured in terms of AP@0.50.
%U https://ieeexplore.ieee.org/abstract/document/9320283
%P 801-808